>The cover, FlyNet, describes a convolutional neural network designed and trained to automatically segment the heart region of a fruit fly from time‐lapsed optical cohe'/> Back Cover: Segmentation of <i >DrosophilaDrosophila heart in optical coherence microscopy images using convolutional neural networks (J. Biophotonics 12/2018)
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Back Cover: Segmentation of DrosophilaDrosophila heart in optical coherence microscopy images using convolutional neural networks (J. Biophotonics 12/2018)

机译:返回封面:分割 果蝇果蝇 光学相干显微镜图像中的心脏使用卷积神经网络(J. BioPhotonics 12/2018)

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>The cover, FlyNet, describes a convolutional neural network designed and trained to automatically segment the heart region of a fruit fly from time‐lapsed optical coherence microscopy (OCM) images. High‐resolution images of the beating fly heart were acquired using a non‐invasive OCM system. The FlyNet model accurately identifies and segments the heart from the images. Morphological and functional parameters can be analyzed to characterize the health status of the fly heart. > Further details can be found in the article by Lian Duan, Xi Qin, Yuanhao He, et al. ( e201800146 )
机译: >盖子,Flynet,描述了一种设计和培训的卷积神经网络,以自动分割果蝇的心脏区域,从时间失效的光学相干显微镜(OCM)图像。 使用非侵入性OCM系统获得跳动蝇心脏的高分辨率图像。 Flynet模型准确地识别和分段心中的核心。 可以分析形态学和功能参数,以表征飞心的健康状况。 > 进一步的详细信息可以在Lian Duan,Xi Qin,Yuanhao He,等人的文章中找到。 ( E201800146 的)

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